Practical Business Statistics, Sixth Edition, is a conceptual, realistic, and
matter-of-fact approach to managerial statistics that carefully maintains-but
does not overemphasize-mathematical correctness. The book offers a deep
understanding of how to learn from data and how to deal with uncertainty while
promoting the use of practical computer applications. This teaches present and
future managers how to use and understand statistics without an overdose of
technical detail, enabling them to better understand the concepts at hand and
to interpret results. The text uses excellent examples with real world data
relating to the functional areas within Business such as finance, accounting,
and marketing. It is well written and designed to help students gain a solid
understanding of fundamental statistical principles without bogging them down
with excess mathematical details.
Autorentext
Andrew F. Siegel holds the Grant I. Butterbaugh Professorship in Quantitative Methods and Finance at the Michael G. Foster School of Business, University of Washington, Seattle, and is also Adjunct Professor in the Department of Statistics. His Ph.D. is in statistics from Stanford University (1977). Before settling in Seattle, he held teaching and/ or research positions at Harvard University, the University of Wisconsin, the RAND Corporation, the Smithsonian Institution, and Princeton University. He has taught statistics at both undergraduate and graduate levels, and earned seven teaching awards in 2015 and 2016. The interest-rate model he developed with Charles Nelson (the Nelson-Siegel Model) is in use at central banks around the world. His work has been translated into Chinese and Russian. His articles have appeared in many publications, including the Journal of the American Statistical Association, the Encyclopedia of Statistical Sciences, the American Statistician, Proceedings of the National Academy of Sciences, Nature, the American Mathematical Monthly, the Journal of the Royal Statistical Society, the Annals of Statistics, the Annals of Probability, the Society for Industrial and Applied Mathematics Journal on Scientific and Statistical Computing, Statistics in Medicine, Biometrika, Biometrics, Statistical Applications in Genetics and Molecular Biology, Mathematical Finance, Contemporary Accounting Research, the Journal of Finance, and the Journal of Applied Probability.
Inhalt
Chapter 1. Introduction
Chapter 2. Data Structures
Chapter 3. Histograms
Chapter 4. Landmark Summaries
Chapter 5. Variability
Chapter 6. Probability
Chapter 7. Random Variables
Chapter 8. Random Sampling
Chapter 9. Confidence Intervals
Chapter 10. Hypothesis Testing
Chapter 11. Correlation and Regression
Chapter 12. Multiple Regression
Chapter 13. Report Writing
Chapter 14. Time Series
Chapter 15. ANOVA
Chapter 16. Nonparametrics
Chapter 17. Chi-Squared Analysis
Chapter 18. Quality Control